Results

cesm2.ssp245

diff_of_means ratio_of_sd monthly_amplitude_ratio_of_means ks_mean_on_coarse_res_with_extremes qqplot_mae acf_mae extremogram_mae
nv.cesm2.ssp245 2.27% 0.916 0.907 0.332 8.147 0.044 0.040
lstm.cesm2.ssp245 2.90% 0.939 0.873 0.301 8.400 0.035 0.033
xgboost.cesm2.ssp245 4.58% 0.908 0.880 0.154 11.010 0.015 0.030
cnn.cesm2.ssp245 9.11% 0.875 0.917 0.255 19.021 0.017 0.047

Time series of the first days

Distribution of daily values by month

QQ Plot

Distribution of the undownscaled value on days with estimated extremes values.

On the x-axis we have the daily mean (standardized). It says Undownscaled value, but is the daily mean after the downscaling. A good idea is to plot the original undownscaled value.

The purpose of this plot is to illustrate the distribution of P(undownscaled value | we predicted an extreme). This is useful because it reveals how much information we can recover concerning extreme events. If the distribution is skewed to the right, it suggests that we’re predicting extreme values only when extreme values have already occurred. Conversely, if the lower tail of the distribution resembles the reanalysis data, it indicates that we can capture short-duration extremes (e.g., brief periods of heavy rainfall, such as an intense downpour lasting an hour before stopping).

Autocorrelogram

Extremogram

cesm2.ssp370

diff_of_means ratio_of_sd monthly_amplitude_ratio_of_means ks_mean_on_coarse_res_with_extremes qqplot_mae acf_mae extremogram_mae
lstm.cesm2.ssp370 2.74% 0.937 0.875 0.250 7.753 0.033 0.040
nv.cesm2.ssp370 3.38% 0.911 0.923 0.250 8.558 0.038 0.044
xgboost.cesm2.ssp370 5.80% 0.894 0.856 0.243 13.069 0.019 0.039
cnn.cesm2.ssp370 8.95% 0.864 0.902 0.280 18.848 0.016 0.037

Time series of the first days

Distribution of daily values by month

QQ Plot

Distribution of the undownscaled value on days with estimated extremes values.

On the x-axis we have the daily mean (standardized). It says Undownscaled value, but is the daily mean after the downscaling. A good idea is to plot the original undownscaled value.

The purpose of this plot is to illustrate the distribution of P(undownscaled value | we predicted an extreme). This is useful because it reveals how much information we can recover concerning extreme events. If the distribution is skewed to the right, it suggests that we’re predicting extreme values only when extreme values have already occurred. Conversely, if the lower tail of the distribution resembles the reanalysis data, it indicates that we can capture short-duration extremes (e.g., brief periods of heavy rainfall, such as an intense downpour lasting an hour before stopping).

Autocorrelogram

Extremogram

cesm2.ssp585

diff_of_means ratio_of_sd monthly_amplitude_ratio_of_means ks_mean_on_coarse_res_with_extremes qqplot_mae acf_mae extremogram_mae
lstm.cesm2.ssp585 1.76% 0.946 0.876 0.378 6.429 0.042 0.044
nv.cesm2.ssp585 2.47% 0.920 0.951 0.436 7.376 0.037 0.036
xgboost.cesm2.ssp585 5.03% 0.894 0.863 0.192 11.845 0.019 0.035
cnn.cesm2.ssp585 8.61% 0.865 0.914 0.358 18.275 0.015 0.034

Time series of the first days

Distribution of daily values by month

QQ Plot

Distribution of the undownscaled value on days with estimated extremes values.

On the x-axis we have the daily mean (standardized). It says Undownscaled value, but is the daily mean after the downscaling. A good idea is to plot the original undownscaled value.

The purpose of this plot is to illustrate the distribution of P(undownscaled value | we predicted an extreme). This is useful because it reveals how much information we can recover concerning extreme events. If the distribution is skewed to the right, it suggests that we’re predicting extreme values only when extreme values have already occurred. Conversely, if the lower tail of the distribution resembles the reanalysis data, it indicates that we can capture short-duration extremes (e.g., brief periods of heavy rainfall, such as an intense downpour lasting an hour before stopping).

Autocorrelogram

Extremogram

ec_earth3.ssp434

diff_of_means ratio_of_sd monthly_amplitude_ratio_of_means ks_mean_on_coarse_res_with_extremes qqplot_mae acf_mae extremogram_mae
lstm.ec_earth3.ssp434 4.14% 0.923 0.909 0.157 8.679 0.011 0.034
nv.ec_earth3.ssp434 7.28% 0.869 0.891 0.222 14.905 0.047 0.051
cnn.ec_earth3.ssp434 9.40% 0.867 0.955 0.215 18.717 0.036 0.047
xgboost.ec_earth3.ssp434 12.41% 0.880 0.862 0.192 24.715 0.018 0.035

Time series of the first days

Distribution of daily values by month

QQ Plot

Distribution of the undownscaled value on days with estimated extremes values.

On the x-axis we have the daily mean (standardized). It says Undownscaled value, but is the daily mean after the downscaling. A good idea is to plot the original undownscaled value.

The purpose of this plot is to illustrate the distribution of P(undownscaled value | we predicted an extreme). This is useful because it reveals how much information we can recover concerning extreme events. If the distribution is skewed to the right, it suggests that we’re predicting extreme values only when extreme values have already occurred. Conversely, if the lower tail of the distribution resembles the reanalysis data, it indicates that we can capture short-duration extremes (e.g., brief periods of heavy rainfall, such as an intense downpour lasting an hour before stopping).

Autocorrelogram

Extremogram

mri_esm2_0.ssp245

diff_of_means ratio_of_sd monthly_amplitude_ratio_of_means ks_mean_on_coarse_res_with_extremes qqplot_mae acf_mae extremogram_mae
lstm.mri_esm2_0.ssp245 2.08% 0.933 0.849 0.288 5.850 0.083 0.035
xgboost.mri_esm2_0.ssp245 -4.18% 0.959 0.843 0.231 9.389 0.085 0.041
nv.mri_esm2_0.ssp245 -7.08% 0.993 0.873 0.282 14.921 0.129 0.036
cnn.mri_esm2_0.ssp245 8.42% 0.872 0.861 0.231 17.291 0.065 0.044

Time series of the first days

Distribution of daily values by month

QQ Plot

Distribution of the undownscaled value on days with estimated extremes values.

On the x-axis we have the daily mean (standardized). It says Undownscaled value, but is the daily mean after the downscaling. A good idea is to plot the original undownscaled value.

The purpose of this plot is to illustrate the distribution of P(undownscaled value | we predicted an extreme). This is useful because it reveals how much information we can recover concerning extreme events. If the distribution is skewed to the right, it suggests that we’re predicting extreme values only when extreme values have already occurred. Conversely, if the lower tail of the distribution resembles the reanalysis data, it indicates that we can capture short-duration extremes (e.g., brief periods of heavy rainfall, such as an intense downpour lasting an hour before stopping).

Autocorrelogram

Extremogram

mri_esm2_0.ssp370

diff_of_means ratio_of_sd monthly_amplitude_ratio_of_means ks_mean_on_coarse_res_with_extremes qqplot_mae acf_mae extremogram_mae
xgboost.mri_esm2_0.ssp370 -1.05% 0.956 0.881 0.262 5.197 0.056 0.038
lstm.mri_esm2_0.ssp370 3.48% 0.930 0.877 0.154 7.791 0.053 0.034
nv.mri_esm2_0.ssp370 -4.53% 1.000 0.916 0.282 10.364 0.111 0.033
cnn.mri_esm2_0.ssp370 9.45% 0.869 0.914 0.229 18.890 0.023 0.034

Time series of the first days

Distribution of daily values by month

QQ Plot

Distribution of the undownscaled value on days with estimated extremes values.

On the x-axis we have the daily mean (standardized). It says Undownscaled value, but is the daily mean after the downscaling. A good idea is to plot the original undownscaled value.

The purpose of this plot is to illustrate the distribution of P(undownscaled value | we predicted an extreme). This is useful because it reveals how much information we can recover concerning extreme events. If the distribution is skewed to the right, it suggests that we’re predicting extreme values only when extreme values have already occurred. Conversely, if the lower tail of the distribution resembles the reanalysis data, it indicates that we can capture short-duration extremes (e.g., brief periods of heavy rainfall, such as an intense downpour lasting an hour before stopping).

Autocorrelogram

Extremogram

mri_esm2_0.ssp434

diff_of_means ratio_of_sd monthly_amplitude_ratio_of_means ks_mean_on_coarse_res_with_extremes qqplot_mae acf_mae extremogram_mae
xgboost.mri_esm2_0.ssp434 -2.87% 0.957 0.873 0.248 6.720 0.074 0.045
lstm.mri_esm2_0.ssp434 3.23% 0.930 0.865 0.244 7.704 0.074 0.036
nv.mri_esm2_0.ssp434 -5.80% 0.995 0.903 0.434 12.612 0.124 0.034
cnn.mri_esm2_0.ssp434 9.27% 0.862 0.894 0.388 18.939 0.051 0.033

Time series of the first days

Distribution of daily values by month

QQ Plot

Distribution of the undownscaled value on days with estimated extremes values.

On the x-axis we have the daily mean (standardized). It says Undownscaled value, but is the daily mean after the downscaling. A good idea is to plot the original undownscaled value.

The purpose of this plot is to illustrate the distribution of P(undownscaled value | we predicted an extreme). This is useful because it reveals how much information we can recover concerning extreme events. If the distribution is skewed to the right, it suggests that we’re predicting extreme values only when extreme values have already occurred. Conversely, if the lower tail of the distribution resembles the reanalysis data, it indicates that we can capture short-duration extremes (e.g., brief periods of heavy rainfall, such as an intense downpour lasting an hour before stopping).

Autocorrelogram

Extremogram